Machine Learning Based Effective Clustering Scheme for Wireless Sensor Networks
2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023, Page: 1-7
2023
- 1Citations
- 3Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
In Wireless Sensor Networks (WSN) sensor nodes are burdened by the exchange of messages caused by successive and recurring re-clustering, which results in power loss. Presently researchers have been concentrating on enhancing the longevity of these nodes due to non-rechargeable batteries fitted in Sensor Nodes (SN). Clustering mechanism has emerged as a desirable subject because, it is predominantly good at conserving the resources especially energy for network activities. In this work, the problem of load balancing and Cluster Head (CH) selection with minimum energy expenditure is proposed. Unsupervised machine learning based k-means algorithm is employed to form cluster and fuzzy based approach is used to select the CH. Simulation results shows the effectiveness of proposed work in terms of energy usage and CH identification delay.
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know